· Generative AI · 1 min read
From Chatbots to Agents: Building AI That Takes Action
Chatbots talk. Agents do. We explain the architecture of Agentic AI, tool use, and the loop of 'Thought, Action, Observation'.
A Chatbot is a sterile box. You ask it to check the weather, and it says “I cannot browse the internet.” An Agent is an LLM given hands. You ask it to check the weather, and it calls the Weather API, parses the JSON, and tells you to bring an umbrella.
The ReAct Loop (Reason + Action)
The core of an agent is a loop:
- Thought: The LLM looks at the user request. ” The user wants to book a flight.”
- Plan: “I need to check availability first.”
- Action: The Agent outputs a structured function call:
search_flights(dest='LHR'). - Observation: The system runs the Python function and feeds the result back to the LLM. “Found 3 flights…“.
- Conclusion: “I found 3 flights, which one do you want?”
Multi-Agent Systems
The cutting edge is now Multi-Agent Systems (like AutoGen). You have a “Coder” agent and a “Reviewer” agent.
- Coder writes a script.
- Reviewer checks it and gives feedback.
- Coder fixes it. They loop until the code works. This simulates a real agile team.
Want AI that does work? We build Agentic workflows that automate real business processes. See a demo.
